Lupus

Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases.

In the context of the recently emerging COVID-19 pandemic, we developed a deep learning model that can be used to predict the inhibitory activity of 3CLpro in severe acute respiratory syndrome coronavirus (SARS-CoV) for unknown compounds during the virtual screening process. This paper proposes a novel deep learning-based method to implement virtual screening with convolutional neural network (CNN...

Is Anti-Müllerian Hormone a Marker of Ovarian Reserve in Young Breast Cancer Patients Receiving a GnRH Analog during Chemotherapy?

INTRODUCTION: Anti-Müllerian hormone (AMH) is the most reliable biomarker of ovarian reserve; however, its role in predicting ovarian recovery after chemotherapy is unclear. Administration of a GnRH analog (GnRHa) during chemotherapy significantly reduces the ovarian failure rate and increases the pregnancy rate. The available data on the behavior of AMH during concurrent administration of chemoth...

SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning.

High resolution magnetic resonance (MR) images are desired in many clinical and research applications. Acquiring such images with high signal-to-noise...

A deep learning-based smartphone platform for cutaneous lupus erythematosus classification assistance: Simplifying the diagnosis of complicated diseases.

TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.

Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-gr...

Antibody Supervised Training of a Deep Learning Based Algorithm for Leukocyte Segmentation in Papillary Thyroid Carcinoma.

The quantity of leukocytes in papillary thyroid carcinoma (PTC) potentially have prognostic and treatment predictive value. Here, we propose a novel m...

Identification of drug combinations on the basis of machine learning to maximize anti-aging effects.

Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated geneti...

Anti-senescent drug screening by deep learning-based morphology senescence scoring.

Advances in deep learning technology have enabled complex task solutions. The accuracy of image classification tasks has improved owing to the establi...

Impact of macular fluid volume fluctuations on visual acuity during anti-VEGF therapy in eyes with nAMD.

OBJECTIVES: To study the effect of repeated retinal thickness fluctuations during the anti-VEGF therapy maintenance phase in neovascular age-related m...

Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19.

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity...

A Machine Learning Approach to Identify Predictors of Potentially Inappropriate Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Use in Older Adults with Osteoarthritis.

Evidence from some studies suggest that osteoarthritis (OA) patients are often prescribed non-steroidal anti-inflammatory drugs (NSAIDs) that are not ...

Perioperative Outcomes of Patients Who Were Not Candidates for Additional Nonsteroidal Anti-inflammatory Drugs in a Multimodal Pain Control Regimen for Total Knee Arthroplasty.

BACKGROUD: Postoperative pain following total knee arthroplasty (TKA) may hamper patients from a rapid recovery and increase perioperative blood loss ...

Cytotoxicity and Anti-inflammatory Cytokinine Activity Study of Three Isolated Novel Compounds of .

OBJECTIVES: The aim of the present study was to isolate and evaluate cytotoxicity and anti-inflammatory activities of new novel compounds isolated fro...

End-to-End Deep Learning Model for Predicting Treatment Requirements in Neovascular AMD From Longitudinal Retinal OCT Imaging.

Neovascular age-related macular degeneration (nAMD) is nowadays successfully treated with anti-VEGF substances, but inter-individual treatment require...

The effect of topical application of meloxicam on inflamed dental pulp.

BACKGROUND/PURPOSE: Effective regulation of the inflammatory process is essential for pulp repair and regeneration. Meloxicam has anti-inflammatory ac...

Identifying longevity associated genes by integrating gene expression and curated annotations.

Aging is a complex process with poorly understood genetic mechanisms. Recent studies have sought to classify genes as pro-longevity or anti-longevity ...

Machine Learning to Quantify Humoral Selection in Human Lupus Tubulointerstitial Inflammation.

In human lupus nephritis, tubulointerstitial inflammation (TII) is associated with expansion of B cells expressing anti-vimentin antibodies (AVAs). T...

Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis.

COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour....

Broad Learning Enhanced H-MRS for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus.

In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectroscopy (H-MRS) to diagnose neuropsychiatric systemic l...

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.

Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, cur...